Multilevel regression analysis reveals healthcare access influences recovery rates in Rwanda, suggesting needed improvements.
Public health surveillance systems are crucial for monitoring disease prevalence in Rwanda's diverse population. A multilevel regression model was employed to analyse data at both individual and district levels, accounting for spatial clustering effects. District-level healthcare access significantly influenced patient recovery rates (OR = 1.25; CI: 1.03-1.51). The multilevel regression analysis highlighted the importance of addressing accessibility disparities to improve clinical outcomes. Enhanced infrastructure and community health education programmes should be prioritised in underserved areas to reduce recovery time. public health surveillance, Rwanda, multilevel regression, clinical outcomes
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Umunyana et al. (2014) studied this question.
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